In contrast to SISR where only a single LR image is used as input, reference-based SISR (RefSR) takes a reference image to assist the SR process. The reference images can be obtained from various sources like photo albums, video frames, and web image searches. Meanwhile, there are several approaches proposed to enhance image textures, such as image aligning and patch matching. Recently, some RefSR methods [1]}, [2]} choose to align the LR and reference images with the assumption that the reference image possesses similar content as the LR image. For instance, Yue et al. [1]} conducted global registration and local matching between the reference and LR images to solve an energy minimization problem. In CrossNet [2]}, optical flow is proposed to align the reference and LR images at different scales, which are later concatenated into the corresponding layers of the decoder. However, these methods assume that the reference image has a good alignment with the LR image. Otherwise, their performance will be significantly influenced. Different from these methods, Zhang et al. [5]} applied patch matching between VGG features of the LR and reference images to adaptively transfer textures from the reference images to the LR images. In TTSR [6]}, Yang et al. proposed a texture transformer network to search and transfer relevant textures from the reference images to the LR images based on the attention mechanisms.
